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Description
The study was to analyze the extent of bacterial transport in a two-dimensional tank under saturated conditions. The experiments were done in a 2-D tank packed with 3,700 in3 of fine grained, homogenous, chemically inert sand under saturated conditions. The tank used for transport was decontaminated by backwashing with 0.6% chlorine solution with subsequent backwashing with chlorine-neutral water (tap water and Na2S2O3) thus ensuring no residual chlorine in the tank. The transport of bacteria was measured using samples collected from ports at vertical distances of 5, 15 and 25 inches (12.7, 38.1 and 63.5 cm) from the surface of the sand on both sides for the 2-D tank. An influent concentration of 105 CFU/mL was set as a baseline for both microbes and the percolation rate was set at 11.37 inches/day using a peristaltic pump at the bottom outlet. At depths of 5, 15 and 25 inches, E. coli breakthroughs were recorded at 5, 17 and 28 hours for the ports on the right side and 7, 17 and 29 hours for the ports on the left sides, respectively. At respective distances Legionella breakthroughs were recorded at 8, 22 and 35 hours for the ports on the right side and 9, 24, 36 hours for the ports on the left side, respectively which is homologous to its pleomorphic nature. A tracer test was done and the visual breakthroughs were recorded at the same depths as the microbes. The breakthroughs for the dye at depths of 5, 15 and 25 inches, were recorded at 13.5, 41 and 67 hours for the ports on the right side and 15, 42.5 and 69 hours for the ports on the left side, respectively. However, these are based on visual estimates and the physical breakthrough could have happened at the respective heights before the reported times. This study provided a good basis for the premise that transport of bacterial cells and chemicals exists under recharge practices.
ContributorsMondal, Indrayudh (Author) / Abbaszadegan, Morteza (Thesis advisor) / Dahlen, Paul (Committee member) / Delgado, Anca (Committee member) / Arizona State University (Publisher)
Created2019
Description
This research focused on the structure-property relationships of a rejuvenator to understand the comprehensive rejuvenation mechanism of aged asphalt binder. Aged asphalt such as recycled asphalt shingles (RAS) and reclaimed asphalt pavement (RAP) contain various amounts of asphalt binder. However, the asphalt binder in RAS and RAP is severely aged and inferior in properties compared to a virgin binder. To address this issue, liquid additives have been used under the general title of rejuvenators. That poses an additional challenge associated with the lack of clear metrics to differentiate between softeners and rejuvenators. Therefore, there is a need for a thorough study of rejuvenators. In this study, diverse-sourced rejuvenators have been used in RAS and RAP-modified binders as well as laboratory-prepared aged binders. The properties of the rejuvenated aged binder were characterized at a macro-level and molecular level. The study showed that the performance of the RAS-modified binder was significantly improved after bio-modification by a bio-rejuvenator.
This study further evaluated laboratory-prepared aged asphalt rejuvenated with different rejuvenators. The results found that oxidized bitumen became soft after adding rejuvenators, regardless of their source. Molecular dynamics simulation showed that the effective rejuvenator restored the molecular conformation and reduced the size of asphaltene nanoaggregates.
The study results showed that due to the specific chemical composition of certain rejuvenators, they may negatively impact the durability of the mixture, especially about its resistance to moisture damage and aging. Computational analysis showed that while the restoration capacity of rejuvenators is related to their penetration into and peptizing of asphaltene nanoaggregates, the durability of the restored aged asphalt is mainly related to the polarizability values of the rejuvenator. Rejuvenators with lower polarizability showed better resistance to aging and moisture damage.
In summary, this study develops the rheology-based indicators which relate to the molecular level phenomenon in the rejuvenation mechanism. The rheology-based indicators, for instance, crossover modulus and crossover frequency differentiated the rejuvenators from recycling agents. Moreover, the study found that rejuvenation efficiency and durability are depended on the chemistry of rejuvenators. Finally, based on the learning of chemistry, a chemically balanced rejuvenator is synthesized with superior rejuvenation properties.
This study further evaluated laboratory-prepared aged asphalt rejuvenated with different rejuvenators. The results found that oxidized bitumen became soft after adding rejuvenators, regardless of their source. Molecular dynamics simulation showed that the effective rejuvenator restored the molecular conformation and reduced the size of asphaltene nanoaggregates.
The study results showed that due to the specific chemical composition of certain rejuvenators, they may negatively impact the durability of the mixture, especially about its resistance to moisture damage and aging. Computational analysis showed that while the restoration capacity of rejuvenators is related to their penetration into and peptizing of asphaltene nanoaggregates, the durability of the restored aged asphalt is mainly related to the polarizability values of the rejuvenator. Rejuvenators with lower polarizability showed better resistance to aging and moisture damage.
In summary, this study develops the rheology-based indicators which relate to the molecular level phenomenon in the rejuvenation mechanism. The rheology-based indicators, for instance, crossover modulus and crossover frequency differentiated the rejuvenators from recycling agents. Moreover, the study found that rejuvenation efficiency and durability are depended on the chemistry of rejuvenators. Finally, based on the learning of chemistry, a chemically balanced rejuvenator is synthesized with superior rejuvenation properties.
ContributorsRajib, Amirul Islam (Author) / Fini, Elham (Thesis advisor) / Yin, Huiming (Committee member) / Kaloush, Kamil (Committee member) / Parast, Mahour (Committee member) / Mamlouk, Michael (Committee member) / El Asmar, Mounir (Committee member) / Arizona State University (Publisher)
Created2020
Description
It is common to use crumb rubber as modifier in bitumen. Good performance of crumb rubber in bitumen has been reported in terms of improving characteristics like higher skid resistance, reducing noise, higher rutting resistance and longevity. However, due to the vulcanization, the polymeric crosslinked structure of crumb rubber suffers from inadequate dispersion and incompatibility in bitumen where storage stability becomes an issue. To solve this problem, partial surface devulcanization of the rubber via chemical and microbial surface activation was examined in this study showing both method can be effective to enhance rubber-bitumen interactions and subsequently storage stability of the rubberized bitumen. To ensure proper surface activation, it is important to thoroughly understand chemo-mechanics of bitumen containing rubber particles as well as underlying interaction mechanism at the molecular level. Therefore, this study integrates a multi-scale approach using density functional theory based computational modeling and laboratory experiments to provide an in-depth understanding of the mechanisms of interaction between surface activated rubber and bitumen. To do so, efficacy of various bio-modifiers was examined and compared it terms of both surface activation capability and durability of resulting rubberized bitumen. It was found that biomodifiers with various compositions can have either synergistic or antagonistic effect onchemo-mechanics of rubberized bitumen. The study was further extended to study the interplay of Polyphosphoric Acid (PPA) and these biomodified rubberized bitumens showing not all modifiers have high synergy with PPA in bitumens. Finally, durability of rubberized bitumen was studied in terms of its resistance to Ultraviolet (UV) aging. It was shown that there is a strong relation between composition of biomodified rubberized bitumen and its resistance to UV-aging.
ContributorsKabir, Sk Faisal (Author) / Fini, Elham (Thesis advisor) / Kaloush, Kamil (Committee member) / Lamanna, Anthony (Committee member) / Delgado, Anca (Committee member) / Poulikakos, Lily (Committee member) / Arizona State University (Publisher)
Created2020
Description
The use of reinforcing fibers in asphalt concrete (AC) has been documented in many studies. Published studies generally demonstrate positive benefits from using mechanically fiber reinforced asphalt concrete (M-FRAC); however, improvements generally vary with respect to the particular study. The widespread acceptance of fibers use in the asphalt industry is hindered by these inconsistencies. This study seeks to fulfill a critical knowledge gap by advancing knowledge of M-FRAC in order to better understand, interpret, and predict the behavior of these materials. The specific objectives of this dissertation are to; (a) evaluate the state of aramid fiber in AC and examine their impacts on the mechanical performance of asphalt mixtures; (b) evaluate the interaction of the reinforcement efficiency of fibers with compositions of asphalt mixtures; (c) evaluate tensile and fracture properties of M-FRAC; (d) evaluate the interfacial shear bond strength and critical fiber length in M-FRAC; and (e) propose micromechanical models for prediction of the tensile strength of M-FRAC. The research approach to achieve these objectives included experimental measurements and theoretical considerations. Throughout the study, the mechanical response of specimens with and without fibers are scrutinized using standard test methods including flow number (AASHTO T 378) and uniaxial fatigue (AASHTO TP 107), and non-standard test methods for fiber extraction, direct tension, semi-circular bending, and single fiber pull-out tests. Then, the fiber reinforcement mechanism is further examined by using the basic theories of viscoelasticity as well as micromechanical models.
The findings of this study suggest that fibers do serve as a reinforcement element in AC; however, their reinforcing effectiveness depends on the state of fibers in the mix, temperature/ loading rate, properties of fiber (i.e. dosage, length), properties of mix type (gradation and binder content), and mechanical test type to characterize M-FRAC. The outcome of every single aforementioned elements identifies key reasons attributed to the fiber reinforcement efficiency in AC, which provides insights to justify the discrepancies in the literature and further recommends solutions to overcome the knowledge gaps. This improved insight will translate into the better deployment of existing fiber-based technologies; the development of new, and more effective fiber-based technologies in asphalt mixtures.
ContributorsNoorvand, Hossein (Author) / Kaloush, Kamil (Thesis advisor) / Underwood, Shane B (Thesis advisor) / Mamlouk, Michael (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2020
Description
The International Energy Agency (IEA) anticipates the global energy demand to grow by more than 25% by 2040, requiring more than $2 trillion a year of investment in new energy supply (IEA, 2018). With power needs increasing as populations grow and climate extremes become more routine, power companies seek to continually increase capacity, improve efficiency, and provide resilience to the power grid, such that they can meet the energy needs of the societies they serve, often while trying to minimize their carbon emissions. Despite significant research dedicated to planning for industrial projects, including power generation projects as well as the pipeline projects that enable power generation and distribute power, there are still endemic cost overruns and schedule delays in large scale power generation projects. This research explores root causes of these seemingly systemic project performance issues that plague power generation projects. Specifically, this work analyzes approximately 770 power and pipeline projects and identifies how project performance indicators (i.e., cost and schedule performance) as well as planning indicators, compare in two regulatory environments, namely nonregulated and regulated markets. This contributes explicit understanding of the relationship between project performance and regulatory environment, both quantitatively and qualitatively, to the pipeline and power project planning and construction bodies of knowledge. Following an understanding of nonregulated versus regulated markets, this research takes a deeper dive into one highly-regulated power sector, the nuclear power sector, and explores root causes for cost overruns and schedule delays. This work leverages gray literature (i.e., newspaper articles) as sources, in order to analyze projects individually (most academic literature presents data about an aggregated set of projects) and understand the public perception of risks associated with such projects. This work contributes an understanding of the risks associated with nuclear power plant construction to the nuclear power plant construction body of knowledge. Ultimately, the findings from this research support improved planning for power and pipeline projects, in turn leading to more predictable projects, in terms of cost and schedule performance, regardless of regulatory environment. This enables power providers to meet the capacity demands of a growing population within budget and schedule.
ContributorsSherman, Rachael Paige (Author) / Parrish, Kristen (Thesis advisor) / Gibson Jr., G. Edward (Committee member) / Lamanna, Anthony (Committee member) / Arizona State University (Publisher)
Created2020
Description
An orthotropic elasto-plastic damage material model (OEPDMM) suitable for impact analysis of composite materials has been developed through a joint research project funded by the Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA). The developed material model has been implemented into LS-DYNA®, a commercial finite element program. The material model is modular comprising of deformation, damage and failure sub-models. The deformation sub-model captures the rate and/or temperature dependent elastic and inelastic behavior via a visco-elastic-plastic formulation. The damage sub-model predicts the reduction in the elastic stiffness of the material. The failure sub-model predicts when there is no more load carrying capacity in the finite element and erosion of the element from the finite element model. Most of the input parameters required to drive OEPDMM are in the form of tabulated data. The deformation sub-model is driven by a set of tabulated stress-strain data for a given strain-rate and temperature combination. The damage sub-model is driven by tabulated damage parameter-strain data. Two failure sub-models have been implemented – Puck Failure Model and Generalized Tabulated Failure Model. Puck Failure Model requires scalar parameters as input whereas, the Generalized Tabulated Failure Model is driven by a set of equivalent failure strain tabulated data. The work presented here focuses on the enhancements made to OEPDMM with emphasis on the background, development, and implementation of the failure sub-models. OEPDMM is verified and validated using a carbon/epoxy fiber reinforced composite. Two validation tests are used to evaluate the failure sub-model implementation - a stacked-ply test carried out at room temperature under quasi-static tensile and compressive loadings, and several high-speed impact tests where there is significant damage and material failure of the impacted panel. Results indicate that developed procedures provide the analyst with a reasonable and systematic approach to building predictive impact simulation models.
ContributorsLoukham, Shyamsunder (Author) / Rajan, Subramaniam SR (Thesis advisor) / Neithalath, Narayanan NN (Committee member) / Mobasher, Barzin BM (Committee member) / Hoover, Christian CH (Committee member) / Liu, Yongming YL (Committee member) / Arizona State University (Publisher)
Created2020
Description
Imagery data has become important for civil infrastructure operation and
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
ContributorsChen, Jiawei (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ContributorsFord, Emily Lucile (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G. (Committee member) / Maneparambil, Kailas (Committee member) / Arizona State University (Publisher)
Created2020
Description
The alternative project delivery methods (APDMs) today are being increasingly used by owner organizations in the architecture, engineering, and construction (AEC) industry. Yet the adoption of these methods can be extremely difficult to accomplish and requires significant change management efforts. To facilitate the APDM adoption, this research aimed to better understand how AEC owner organizations have changed from only using the design-bid-build method to also successfully implementing APDMs from an organizational change perspective. This research utilized a literature review, survey and interviews to fulfill the research objectives. The dissertation follows a three paper format. The first paper focuses on identifying organizational change management (OCM) practices that, when effectively executed, lead to increased success rates of adopting APDMs in owner AEC organizations. The results of the first paper indicated that the five OCM practices with the strongest correlations to successful APDM adoption were realistic timeframe, effective change agents, workload adjustments, senior-leadership commitment, and sufficient change-related training. The second paper focuses on investigating AEC employees’ reactions to the adoption of APDMs. The findings of the second paper revealed that employees in AEC organizations react favorably to adopting a change in their project delivery systems. The findings further revealed that increasing the use of OCM practices is related to decreased employee resistance to change. The third paper aimed to provide guidelines detailing on how to lead APDM adoption. The findings of the third paper indicated that there was a general sequence of four implementation phases, which were preparing and planning, pilot project testing, expanding to the intended scale, and sustaining and evaluating. The phases include specific OCM practices that increase the probability of successful APDM adoption. The dissertation results can help in guiding the senior managers of construction organizations and OCM consultants to effectively implement APDMs for the first time in the construction sector.
ContributorsAldossari, Khaled Medath (Author) / Sullivan, Kenneth T. (Thesis advisor) / Hurtado, Kristen C (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2020
Description
The construction industry generates tremendous amounts of data every day. Data can inform practitioners to increase their project performance as well as the quality of the resulting built environment. The data gathered from each stage has unique characteristics, and processing them to the appropriate information is critical. However, it is often difficult to measure the impact of the research across project phases (i.e., planning, design, construction, operation and maintenance, and end-of-life). The goal of this dissertation is to present how industry data can be used to make an impact on construction practices and test a suite of methods to measure the impact of construction research across project phases. The dissertation provides examples of impactful research studies for each project phase to demonstrate the collection and utilization of data generated from each stage and to assess the potential tangible impact on construction industry practices. The completed studies presented both quantitative and qualitative analyses. The first study focuses on the planning phase and provides a practice to improve frond end planning (FEP) implementation by developing the project definition rating index (PDRI) maturity and accuracy total rating system (MATRS). The second study uses earned value management system (EVMS) information from the design and construction phases to support reliable project control and management. The dissertation then provides a third study, this time focusing on the operations phase and comparing the impact of project delivery methods using the international roughness index (IRI). Lastly, the end-of-life or decommissioning phase is tackled through a study that gauges the monetary impact of the circular economy concept applied to reuse construction and demolition (C&D) waste. This dissertation measures the impact of the research according to the knowledge mobilization (KMb) theory, which illustrates the value of the work to the public and to practitioners.
ContributorsCho, Namho (Author) / El Asmar, Mounir (Thesis advisor) / Gibson, George (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2020